Genetic GIScience

Toward a Place-Based Synthesis of the Genome, Exposome, and Behavome

Datos Bibliográficos

ID8378447
AutoresGeoffrey M Jacquez (0000-0001-7352-4233, University at Buffalo, State University of New York, autor de correspondencia), Clive E Sabel (0000-0001-9180-4861, University of Bristol, autor de correspondencia), Shi Chen (0000-0003-0537-3730, University at Buffalo, State University of New York, autor de correspondencia), Chen Shi (0000-0003-4916-3655)
Año2015
Volumen105
Número3
Páginas454-472
Fecha de publicación2015-05-04
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaAnnals of the Association of American Geographers (JOURNAL)
Identificadores de la revistaISSN: 0004-5608 • E-ISSN: 1467-8306
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00045608.2015.1018777
PMID26339073
OpenAlexW2140753801
IdiomaEN
Citas recibidas8
Referencias citadas61

The exposome, defined as the totality of an individual's exposures over the life course, is a seminal concept in the environmental health sciences. Although inherently geographic, the exposome as yet is unfamiliar to many geographers. This article proposes a place-based synthesis, genetic geographic information science (Genetic GISc) that is founded on the exposome, genome+ and behavome. It provides an improved understanding of human health in relation to biology (the genome+), environmental exposures (the exposome), and their social, societal and behavioral determinants (the behavome). Genetic GISc poses three key needs: First, a mathematical foundation for emergent theory; Second, process-based models that bridge biological and geographic scales; Third, biologically plausible estimates of space-time disease lags. Compartmental models are a possible solution; this article develops two models using pancreatic cancer as an exemplar. The first models carcinogenesis based on the cascade of mutations and cellular changes that lead to metastatic cancer. The second models cancer stages by diagnostic criteria. These provide empirical estimates of the distribution of latencies in cellular states and disease stages, and maps of the burden of yet to be diagnosed disease. This approach links our emerging knowledge of genomics to cancer progression at the cellular level, to individuals and their cancer stage at diagnosis, to geographic distributions of cancer in extant populations. These methodological developments and exemplar provide the basis for a new synthesis in health geography: genetic geographic information science

Biology · Data science · Disease · Evolutionary biology · Exposome · Extant taxon · Pathology · Computer Science · Data-Driven Disease Surveillance · Genetics · Health, Environment, Cognitive Aging · Medicine · Nutritional Studies and Diet